Search Results (616)

Drought is a leading constraint on plant productivity and will intensify with climate change. Plant acclimation emerges from a multilayered regulatory system that integrates signaling, transcriptional reprogramming, RNA-based control, and chromatin dynamics. Within this hierarchy, non-coding RNAs (ncRNAs) provide a unifying regulatory layer; microRNAs (miRNAs) modulate abscisic acid and auxin circuits, oxidative stress defenses, and root architecture. This balances growth with survival under water-deficient conditions. Small interfering RNAs (siRNAs) include 24-nucleotide heterochromatic populations that operate through RNA-directed DNA methylation, which positions ncRNA control at the transcription–chromatin interface. Long non-coding RNAs (lncRNAs) act in cis and trans, interact with small RNA pathways, and can serve as chromatin-associated scaffolds. Circular RNAs (circRNAs) are increasingly being detected as responsive to drought. Functional studies in Arabidopsis and maize (e.g., ath-circ032768 and circMED16) underscore their regulatory potential. This review consolidates ncRNA biogenesis and function, catalogs drought-responsive modules across model and crop species, especially cereals, and outlines methodological priorities, such as long-read support for isoforms and back-splice junctions, stringent validation, and integrative multiomics. The evidence suggests that ncRNAs are tractable entry points for enhancing drought resilience while managing growth–stress trade-offs. Full article

The nourishment of the human population depends on a handful of staple crops, such as maize, rice, wheat, soybeans, potatoes, tomatoes, and cassava. However, all crop plants are affected by at least one virus causing diseases that reduce yield, and in some parts of the world, this leads to food insecurity. Conventional management practices need to be improved to incorporate recent scientific and technological developments such as antiviral gene silencing, the use of double-stranded RNA (dsRNA) to activate an antiviral response, and nanobiotechnology. dsRNA with antiviral activity disrupt viral replication, limit infection, and its use represents a promising option for virus management. However, currently, the biggest limitation for viral diseases management is that dsRNA is unstable in the environment. This review is focused on the potential of nanoparticles and nanocarriers to deliver dsRNA, enhance stability, and activate antiviral gene silencing. Effective carriers include metal-based nanoparticles, including silver, zinc oxide, and copper oxide. The stability of dsRNA and the efficiency of gene-silencing activation are enhanced by nanocarriers, including layered double hydroxides, chitosan, and carbon nanotubes, which protect and transport dsRNA to plant cells. The integration of nanocarriers and gene silencing represents a sustainable, precise, and scalable option for the management of viral diseases in crops. It is essential to continue interdisciplinary research to optimize delivery systems and ensure biosafety in large-scale agricultural applications. Full article

The fruiting stage of soybean (Glycine max L.) is critical for determining both its yield and quality, thereby influencing global production. While some studies have provided partial explanations for the occurrence of Fusarium species on soybean seeds and pods, the fungal diversity affecting soybean pods in Sichuan Province, a major soybean cultivation region in Southwestern China, remains inadequately understood. In this study, 182 infected pods were collected from a maize–soybean relay strip intercropping system. A total of 10 distinct pod-infecting fungal genera (132 isolates) were identified, and their pathogenic potential on soybean seeds and pods was evaluated. Using morphological characteristics and DNA barcode markers, we identified 43 Fusarium isolates belonging to 8 species, including F. verticillioides, F. incarnatum, F. equiseti, F. proliferatum, F. fujikuroi, F. oxysporum, F. chlamydosporum, and F. acutatum through the analysis of the translation elongation factor gene (EF1-α) and RNA polymerases II second largest subunit (RPB2) gene. Multi-locus phylogenetic analysis, incorporating the Internal Transcribed Spacer (rDNA ITS), β-tubulin (β-tubulin), Glyceraldehyde 3-phosphate dehydrogenase (GADPH), Chitin Synthase 1 (CHS-1), Actin (ACT), Beta-tubulin II (TUB2), and Calmodulin (CAL) genes distinguished 37 isolates as 6 Colletotrichum species, including C. truncatum, C. karstii, C. cliviicola, C. plurivorum, C. boninense, and C. fructicola. Among these, F. proliferatum and C. fructicola were the most dominant species, representing 20.93% and 21.62% of the isolation frequency, respectively. Pathogenicity assays revealed significant damage from both Fusarium and Colletotrichum isolates on soybean pods and seeds, with varying isolation frequencies. Of these, F. proliferatum, F. acutatum, and F. verticillioides caused the most severe symptoms. Similarly, within Colletotrichum genus, C. fructicola was the most pathogenic, followed by C. truncatum, C. karstii, C. cliviicola, C. plurivorum, and C. boninense. Notably, F. acutatum, C. cliviicola, C. boninense, and C. fructicola were identified for the first time as pathogens of soybean pods under the maize–soybean strip intercropping system in Southwestern China. These findings highlight emerging virulent pathogens responsible for soybean pod decay and provide a valuable foundation for understanding the pathogen population during the later growth stages of soybean. Full article

The Northeast region in China is a major maize-producing area; however, low-temperature stress (TS) limits maize (Zea mays L.) seed germination, affecting population establishment and yield. In order to systematically explore the regulation mechanism of maize radicle which is highly sensitive to low-temperature environment response to TS, seeds of ZD958 and DMY1 were used to investigate germination responses under 15 °C (control) and 5 °C (TS) conditions. Phenotypic, physiological, transcriptomic, and metabolomic analyses were conducted on the radicles after 48 h of TS treatment. TS caused reactive oxygen species (ROS) imbalance and oxidative damage in radicle cells, inhibiting growth and triggering antioxidant defenses. Integrated transcriptomic and metabolomic analyses revealed that flavonoid metabolism may play a pivotal role in radicle responses to TS. Compared with the control treatment, ZD958 and DMY1 under TS treatment significantly increased (p < 0.01) the total flavonoid content, total antioxidant capacity, 4-coumarate-CoA ligase activity, and dihydroflavonol 4-reductase activity by 15.99% and 16.01%, 18.41% and 18.54%, 63.54% and 31.16%, and 5.09% and 7.68%, respectively. Despite genotypic differences, both followed a shared regulatory logic of “low-temperature signal-driven—antioxidant redirection—functional synergy.” This enabled ROS scavenging, redox balance, and antioxidant barrier formation, ensuring basal metabolism and radicle development. Full article

Background: This study aims to systematically evaluate the genetic divergence among 200 fresh maize inbred lines using both phenotypic and molecular markers, and to compare the efficacy of these two approaches for genetic classification. Methods: Phenotypic clustering analysis was conducted based on eight key agronomic traits, including plant height and ear length. Additionally, molecular analysis was performed using 40 Simple Sequence Repeat (SSR) primer pairs, resulting in the generation of 230 polymorphic alleles. The polymorphism information content (PIC) was calculated to evaluate the discriminatory power of the markers. Results: Phenotypic analysis categorized the inbred lines into four groups, comprising 25, 38, 97, and 40 lines, respectively, with benchmark lines distributed across Groups I and III. SSR analysis revealed a high level of genetic diversity, with an average of 5.75 alleles per locus and a mean polymorphic information content (PIC) of 0.70. Molecular grouping further divided the population into four distinct clusters, representing 26.5%, 51.0%, 14.0%, and 8.5% of the total, which exhibited different distribution patterns compared to the phenotypic grouping. The distribution of benchmark lines across various molecular groups confirmed their genetic divergence. Conclusions: SSR-based clustering demonstrated superior robustness and reliability compared to phenotypic grouping for genetic discrimination. These findings confirm the substantial genetic diversity present in fresh maize inbred lines and support the preferential use of SSR markers in maize breeding programs for precise genetic characterization. Full article

Intercropping, especially legume-cereal systems, is a mixed farming approach that can improve agricultural resilience by addressing challenges such as soil degradation, biodiversity loss, and global change, all while promoting the sustainable production of protein-rich and nutritious food. However, its adoption in industrialized countries remains limited due to economic and technical challenges, as well as a fragmented understanding of soil–plant-microbe interactions, which hinders its complete optimization. This article provides an overview of the current situation and future perspectives on the importance of legume–cereal intercropping, with examples such as common bean–maize, soybean–maize, alfalfa–corn–rye, and legumes–pulses–little millet systems. These combinations highlight how intercropping can improve nutrient cycling, increase root growth, forage and grain yield, suppress soil-borne diseases, and promote soil microbial population and enzymatic activity. While it offers environmental benefits, practical challenges such as system design, management complexity, and cost-effectiveness must be addressed to encourage wider adoption. In preparing this review, we synthesized studies published between 2000 and 2025, with a particular emphasis on recent research from China and Southeast Asia. We also considered broader intercropping contexts, including energy crops, agroforestry systems, rice paddy co-cultures, and phytoremediation approaches. The review also highlights legume–cereal as a solution to sustainable soil management, ecosystem health, and the potential for increased nutritional food production in developed countries. Full article

Wheat germplasm resources are an important material foundation for genetic improvement. In this study, 170 wheat germplasm resources were used from China, the International Maize and Wheat Improvement Center (CIMMYT), Europe (France, Finland, and Sweden), the United States, Canada, and Australia. Seven nutritional quality traits were evaluated for the 2019–2020 and 2020–2021 cropping seasons. The coefficient of variability for seven nutritional quality traits ranged from 6.99% to 30.65%. The average of genetic diversity (Shannon–Wiener diversity index, H′) was 1.87. The results showed that the average frequency of high-throughput competitive allele-specific PCR (KASP) markers was 69.4% on 17 KASP markers related to seven nutritional quality traits, the average of polymorphic information content (PIC) was 0.308, and the genetic effects were from 0.01% to 18.46%. One hundred and seventy wheat germplasm resources were classified into five groups at ΔK = 5 by genetic structure analysis. The first group comprised 62 germplasm resources (36.47%), the second group included 41 germplasm resources (24.11%), the third group contained 20 germplasm resources (11.76%), the fourth group contained 20 germplasm resources (11.76%), and the fifth group had 29 germplasm resources (17.06%). Germplasm resources from CIMMYT and China were found in the first group and the second group, accounting for 56.45% and 65.85%, respectively, while European germplasm resources constituted 50% of those within the fourth group. Five favorable haplotypes were identified, which were located on chromosomes 4A, 6A, 6B, and 7A: G4A1, G4A2, G6A, G6B, and G7A. Their genetic effects were 8.71%, 8.41%, 1.00%, 18.20%, and 1.16%, respectively. In the meantime, we found 12 significant SNPs of seven nutritional quality traits using haplotype analysis. The frequency of favorable haplotypes in the population ranged from 3.53% to 62.35%. Five haplotypes, G4A1, G4A2, G6A, G6B, and G7A, were beneficial, and their genetic effects were positive. Furthermore, the results offered favorable haplotypes and germplasm resources for enhancing nutritional quality. Full article

Climate change is expected to reduce water availability during cropping season, while growing populations and rising living standards will increase the global water demand. This creates an urgent need for national water management tools to optimize water allocation. In particular, agriculture requires targeted approaches to improve efficiency. Alongside field measurements and remote sensing, agro-hydrological models have emerged as a particularly valuable resource for assessing and managing agricultural water demand. This study introduces WaterCROPv2, a state-of-the-art agro-hydrological model designed to estimate national-scale irrigation water demand while effectively balancing accuracy with practical data requirements. WaterCROPv2 incorporates innovative features such as hourly time-step computations, advanced rainwater canopy interception modeling, detailed soil-dependent leakage dynamics, and localized daily evapotranspiration patterns based on meteorological data. Through comprehensive analyses, WaterCROPv2 demonstrates significantly enhanced reliability in estimating irrigation water needs across various climatic regions, particularly under contrasting dry and wet conditions. Validation against independent data from the Italian National Institute of Statistics (ISTAT) for maize cultivation in Italy in 2010 confirms the model’s accuracy and underscores its potential for broader international applications. A spatial analysis further reveals that the estimation errors align closely with regional precipitation patterns: the model tends to slightly underestimate irrigation needs in the wetter northern regions, whereas it somewhat overestimates demand in the drier southern areas. WaterCROPv2 has also been used to analyze irrigation water requirements for maize cultivation in Italy from 2005 to 2015, highlighting its significant potential as a strategic decision-support tool. The model identifies optimal cultivation areas, such as the Pianura Padana, where the irrigation requirements do not exceed 200 mm for the entire maize growing period, and unsuitable regions, such as Salentino, where over 500 mm per season are required due to the local climatic conditions. In addition, estimates of the water volumes required for the current extent of maize cultivation show that the Pianura Padana region demands nearly three times the amount of water used in the Salentino area. The model has also been used to identify regions where adopting efficient irrigation technologies could lead to substantial water savings. With micro-irrigation currently covering less than 18% of irrigated land, simulations suggest that a complete transition to this system could reduce the national water demand by 21%. Savings could reach 30–40% in traditionally water-rich regions that rely on inefficient irrigation practices but are expected to be increasingly exposed to temperature increases and precipitation shifts. The analysis shows that those regions currently lacking adequate irrigation infrastructure stand to gain the most from targeted irrigation system investments but also highlights how incentives where micro-irrigation is already widespread can provide further 5–10% savings. Full article

Agronomic advancements have led to significant increases in maize yield per hectare in Northeast China, primarily through improved density tolerance. However, the genetic mechanism underlying grain yield responses to density stress remains poorly understood. Here, a population of 193 recombinant inbred lines (RILs) derived from the cross between ZM058 and PH1219 was employed to identify quantitative trait loci (QTLs) under two planting densities across three locations over two years. Six yield-related traits were investigated: ear tip-barrenness length (BEL), cob diameter (CD), ear diameter (ED), ear length (EL), kernel number per row (KNR), and kernel row number (KRN). These traits exhibited distinct and divergent responses to density stress, with the values of CD, ED, EL, KNR and KRN decreasing as planting density increased, except for BEL. A total of 81 QTLs were identified for these traits: 39 were unique to low planting density, 22 to high planting density, and 20 were shared across both conditions. Additionally, nine QTL clusters implicated in the development of multiple traits were detected. The results indicate that planting density significantly affects yield traits, primarily through the interaction of numerous minor QTLs with multiple effects. This insight enhances our understanding of the genetic basis of yield-related traits and provides valuable guidance for breeding high-density-tolerant varieties. Full article

Rice, wheat, and maize are important food grains consumed by most of the population in Asian countries (like India, Japan, Singapore, Malaysia, China, and Thailand). These crops’ production is affected by biotic and abiotic factors that cause diseases in several parts of the crops (including leaves, stems, roots, nodes, and panicles). A severe infection affects the growth of the plant, thereby undermining the economy of a country, if not detected at an early stage. This may cause extensive damage to crops, resulting in decreased yield and productivity. Early safeguarding methods are overlooked because of farmers’ lack of awareness and the variety of crop diseases. This causes significant crop damage and can consequently lower productivity. In this manuscript, a lightweight vision transformer (MaxViT) with 814.7 K learnable parameters and 85 layers is designed for classifying crop diseases in paddy and wheat. The MaxViT DNN architecture consists of a convolutional block attention module (CBAM), squeeze and excitation (SE), and depth-wise (DW) convolution, followed by a ConvNeXt module. This network architecture enhances feature representation by eliminating redundant information (using CBAM) and aggregating spatial information (using SE), and spatial filtering by the DW layer cumulatively enhances the overall classification performance. The proposed model was tested using a paddy dataset (with 7857 images and eight classes, obtained from local paddy farms in Lalgudi district, Tiruchirappalli) and a wheat dataset (with 5000 images and five classes, downloaded from the Kaggle platform). The model’s classification performance for various diseases has been evaluated based on accuracy, sensitivity, specificity, mean accuracy, precision, F1-score, and MCC. During training and testing, the model’s overall accuracy on the paddy dataset was 99.43% and 98.47%, respectively. Training and testing accuracies were 94% and 92.8%, respectively, for the wheat dataset. Ablation analysis was carried out to study the significant contribution of each module to improving the performance. It was found that the model’s performance was immune to the presence of noise. Additionally, there are a minimal number of parameters involved in the proposed model as compared to pre-trained networks, which ensures that the model trains faster. Full article

Aflatoxins (AFs), harmful secondary metabolites produced by the genus Aspergillus, particularly Aspergillus flavus and Aspergillus parasiticus, are one of the best-known potent mycotoxins, posing a significant risk to public health. The primary type, especially aflatoxin B₁ (AFB₁), is a potent carcinogen associated with liver cancer, immunosuppression, and other health problems. Environmental factors such as high temperatures, humidity, and inadequate storage conditions promote the formation of aflatoxin in staple foods such as maize, peanuts, and rice. Immunocompromised individuals, including those with HIV/AIDS, hepatitis, cancer, or diabetes, are at increased risk due to their reduced detoxification capacity and weakened immune defenses. Chronic exposure to AF in these populations exacerbates liver damage, infection rates, and disease progression, particularly in developing countries and moderate-income populations where food safety regulations are inadequate and reliance on contaminated staple foods is widespread. Biomarkers such as aflatoxin-albumin complexes, urinary aflatoxin M₁, and aflatoxin (AF) DNA adducts provide valuable insights but remain underutilized in resource-limited settings. Despite the globally recognized health risk posed by AF, research focused on monitoring human exposure remains limited, particularly among immunocompromised individuals. This dynamic emphasizes the need for targeted studies and interventions to address the particular risks faced by immunocompromised individuals. This review provides an up-to-date overview of AF exposure in immunocompromised populations, including individuals with cancer, hepatitis, diabetes, malnutrition, pregnant women, and the elderly. It also highlights exposure pathways, biomarkers, and biomonitoring strategies, while emphasizing the need for targeted interventions, advanced diagnostics, and policy frameworks to mitigate health risks in these vulnerable groups. Addressing these gaps is crucial to reducing the health burden and developing public health strategies in high-risk regions. Full article

The use of chemical pesticides in agriculture has led to the development of resistant pest populations, posing a challenge to long-term pest management. This review aims to evaluate the scientific literature on the individual and combined use of baculoviruses with conventional chemical and biological insecticides to combat Plutella xylostella, Spodoptera exigua, and Spodoptera frugiperda in broccoli, tomato, and maize crops. Notable findings include that both individual Plutella xylostella nucleopolyhedrovirus (PxNPV) and the combination of Plutella xylostella granulovirus (PxGV) and azadirachtin at a low dose effectively control Plutella xylostella; both combinations of Spodoptera exigua multiple nucleopolyhedrovirus (SeMNPV) with emamectin benzoate and chlorfenapyr reduced resistance in Spodoptera exigua and increased the efficacy of the insecticides; and the combination of Spodoptera frugiperda nucleopolyhedrovirus (SfMNPV) and spinetoram is effective against Spodoptera frugiperda. Integrating baculoviruses into pest management strategies offers a promising approach to mitigate the adverse effects of chemical pesticides, such as resistance development, health risks, and environmental damage. However, there remains a broad spectrum of research opportunities regarding the use of baculoviruses in agriculture. Full article

Tillage is a major factor influencing soil biological communities, particularly earthworms, which play a key role in soil structure and nutrient cycling. To address soil degradation, less-intensive tillage practices are increasingly being adopted globally and have shown positive effects on earthworm populations when applied consistently over extended periods. However, understanding of the earthworm population dynamics in the period following the implementation of changes in tillage practices remains limited. This three-year field study (2021–2023) investigates earthworm populations during the early transition phase (4–6 years) following the conversion from conventional ploughing to conservation (<8 cm depth, with residue retention) and no-tillage systems in a temperate arable system in central Slovenia. Earthworms were sampled annually in early October from three adjacent fields, each following the same three-year crop rotation (maize—winter cereal + cover crop—soybeans), using a combination of hand-sorting and allyl isothiocyanate (AITC) extraction. Results showed that reduced tillage practices significantly increased both earthworm biomass and abundance compared to conventional ploughing. However, a significant interaction between tillage and year was observed, with a sharp decline in earthworm abundance and mass in 2022, likely driven by a combination of 2022 summer tillage prior to cover crop sowing and extreme drought conditions. Juvenile earthworms were especially affected, with their proportion decreasing from 62% to 34% in ploughed plots and from 63% to 26% in conservation tillage plots. Despite interannual fluctuations, no-till showed the lowest variability in earthworm population. Long-term monitoring is essential to disentangle management and environmental effects and to inform resilient soil management strategies. Full article

Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). The method is designed for non-specialist users and is fully integrated within the QGIS platform. The proposed approach involves calculating three vegetation indices—Excess Green (ExG), Green Leaf Index (GLI), and Modified Green-Red Vegetation Index (MGRVI)—based on a standardized orthomosaic generated from RGB images collected via UAV. Subsequently, an unsupervised k-means clustering algorithm was applied to divide the field into five vegetation vigor classes. Within each class, 25% of the pixels with the lowest average index values were preliminarily classified as damaged. A dedicated QGIS plugin enables drone data analysts (Drone Data Analysts—DDAs) to adjust index thresholds, based on visual interpretation, interactively. The method was validated on a 50-hectare maize field, where 7 hectares of damage (15% of the area) were identified. The results indicate a high level of agreement between the automated and manual classifications, with an overall accuracy of 81%. The highest concentration of damage occurred in the “moderate” and “low” vigor zones. Final products included vigor classification maps, binary damage masks, and summary reports in HTML and DOCX formats with visualizations and statistical data. The results confirm the effectiveness and scalability of the proposed RGB-based procedure for crop damage assessment. The method offers a repeatable, cost-effective, and field-operable alternative to multispectral or AI-based approaches, making it suitable for integration with precision agriculture practices and wildlife population management. Full article

Water scarcity, exacerbated by climate change, population growth, and competing sectoral demands, poses a major threat to agricultural sustainability, particularly in irrigated regions such as the Nile Delta in Egypt. Addressing this challenge requires innovative approaches to evaluate irrigation performance despite the limitations in ground data availability. Traditional assessment methods are often costly, labor-intensive, and reliant on field data, limiting their scalability, especially in data-scarce regions. This paper addresses this gap by presenting a comprehensive and scalable framework that employs publicly accessible satellite data to map crop types and subsequently assess irrigation performance without the need for ground truthing. The framework consists of two parts: First, crop mapping, which was conducted seasonally between 2015 and 2020 for the four primary crops in the Nile Delta (rice, maize, wheat, and clover). The WaPOR v2 Land Cover Classification layer was used as a substitute for ground truth data to label the Landsat-8 images for training the random forest algorithm. The crop maps generated at 30 m resolution had moderate to high accuracy, with overall accuracy ranging from 0.77 to 0.80 in summer and 0.87–0.95 in winter. The estimated crop areas aligned well with national agricultural statistics. Second, based on the mapped crops, three irrigation performance indicators—adequacy, reliability, and equity—were calculated and compared with their established standards. The results reveal a good level of equity, with values consistently below 10%, and a relatively reliable water supply, as indicated by the reliability indicator (0.02–0.08). Average summer adequacy ranged from 0.4 to 0.63, indicating insufficient supply, whereas winter values (1.3 to 1.7) reflected a surplus. A noticeable improvement gradient was observed for all indicators toward the north of the delta, while areas located in the delta’s new lands consistently displayed unfavorable conditions in all indicators. This approach facilitates the identification of regions where agricultural performance falls short of its potential, thereby offering valuable insights into where and how irrigation systems can be strategically improved to enhance overall performance sustainably. Full article